Detect Insider Fraud Before It Happens
Every year, insider fraud costs Indian banks thousands of crores. Most cases are discovered 12–18 months after the damage is done; by which point it is too late. SentinelIQ watches every privileged user, every action, in real time. The moment behaviour deviates from baseline, investigators know.
Average detection lag for insider fraud: the time attackers operate undetected
Of all banking fraud events traced back to internal actors with valid access
False positive rate on SentinelIQ's 3-model ensemble
The Problem
Rule-based systems don't catch the employee who already knows where the cameras are.
Traditional fraud controls work on known patterns. Blocklists, velocity thresholds, static rules: they stop the fraud you have already seen. Insider threats are different. The attacker has valid credentials, legitimate access, and years of institutional knowledge about exactly how systems are monitored.
By the time a rule fires, the data has already left the building. SentinelIQ builds a statistical fingerprint of what each employee's normal behaviour looks like — and flags deviation the moment it begins, not 12 months later when an audit cycle finally catches up.
Privilege Escalation
Exploiting temporary permissions or misconfigured roles to reach systems outside their clearance level, often done incrementally to avoid triggering single-event rules.
Bulk Data Exfiltration
Anomalous download volumes compressed into narrow time windows. Often timed around resignation notices or performance reviews, when monitoring attention is elsewhere.
Off-Hours Access
Logins and high-value transactions at 2am from unfamiliar device fingerprints. Invisible to day-shift supervisors and nearly impossible to catch with manual review cycles.
Cross-Department Queries
A teller querying treasury systems. An analyst accessing HR payroll data. Each access might look legitimate in isolation; only behavioural baseline comparison reveals the pattern.
From Event to Evidence in Under 30 Seconds
50ms to score. Under a minute for an analyst to have a verdict.
Built for High-Stakes Detection
Production-grade ML pipeline running end-to-end on every event.
Real-Time Monitoring
Continuous behavioural event stream scored in under 50ms. Every login, transaction, and file access evaluated against a per-user baseline.
3-Model Ensemble
Isolation Forest for point anomalies, LSTM Autoencoder for temporal sequences, and XGBoost for supervised pattern scoring, combined into a single risk signal.
SHAP Explainability
Every alert ships with feature-level SHAP attributions from TreeExplainer. Analysts see exactly which behaviours drove the risk score.
Investigator View
When the score breaks threshold, the analyst sees the full picture.
Not a severity flag: a complete evidence package. Risk score, SHAP explanation, 30 days of behavioural history, and every linked prior incident for that user.
Event scored in real time
Every user action (login, transaction, or file access) is encoded into 8 engineered features and passed through all three models simultaneously in under 50ms.
Ensemble threshold triggers the alert
When the weighted ensemble score (IF 0.3 + LSTM 0.4 + XGB 0.3) exceeds 70, an alert is created with severity level, user context, and a frozen snapshot of the triggering event.
SHAP explains why the score fired
TreeExplainer runs on the XGBoost component and returns the top 5 feature contributions — positive values push the score up, negative values pulled it down.
Analyst reviews the full evidence package
The investigator sees the risk score, SHAP breakdown, 30 days of risk history, and all linked prior alerts for the user — then labels it TP/FP to improve future model accuracy.
The next insider fraud attempt is already in progress.
SentinelIQ surfaces it before the damage is done.